Job Information
McKinsey & Company Data Scientist II - Agriculture Analytics (Machine Learning in Denver, Colorado
Analytics Data Scientist II - Agriculture Analytics (Machine Learning / Geospatial) Job ID: 95364
Do you want to work on complex and pressing challenges-the kind that bring together curious, ambitious, and determined leaders who strive to become better every day? If this sounds like you, you've come to the right place. Your Impact
You'll work with McKinsey's agricultural analytics team (ACRE) in Denver, Colorado.
Our Agriculture Practice advises agribusiness, consumer food, government, and investor clients on strategic, marketing & sales, and operations issues, helping support industry-shaping decisions that impact the future of global food production.
Within this group, ACRE is a team of expert consultants, data scientists, and engineers focused on bringing cutting-edge analytics to our agribusiness clients. We apply real-time data and advanced analytics - generally predictive or geospatial in nature - to solve the biggest problems currently facing global agricultural markets, driving insights at the micro and macro levels.
ACRE is an agile team within the firm whose goal is to use the latest analytical methods, incubate new technologies and drive innovative ways to develop new opportunities for the firm to make significant and lasting client impact - redefining what it means to provide the "best of the firm" to our clients.
Your Growth
You will leverage industry knowledge and analytical expertise to provide insights both to clients as part of client service teams and within the ACRE team by strengthening the core products and algorithms we build for clients. You can expect to split your time delivering impact at clients (including up to 50% travel to client sites) and building up ACRE's core analytical offerings.
As a member of client service teams, you will leverage your creativity and problem-solving skills to tackle clients' most pressing issues using an analytical lens, meeting client needs and communicating your work to executive audiences. Issues to solve could include determining the suitability (now and in the future given impacts of climate change) of growing different types of crops across different geographies, using local agronomic data and satellite imagery to determine market potential for input products, or optimizing farm operations using yield forecasting. Your work will often include heavy geospatial components and/or predictive analytics components.
When working internally with the ACRE team, you will build innovative algorithms and products (what we call "IP development") to best meet our most common client needs. You will use global data such as geospatial, biological, economic and climatological data to develop proprietary solutions. You will work with our engineers to design new interfaces to deliver faster, more impactful insights to our clients.
Along the way, you will receive best-in-class training in structuring business problems and serving as a client adviser and have opportunities to work closely with and learn from our senior agriculture practitioners and industry players that are shaping the future of food production. You will get access to unparalleled career acceleration, with a huge amount of ownership and responsibility from the get-go in a collaborative, diverse, non-hierarchical environment. You will get the opportunity to travel to client sites, locally and around the world. Lastly, you will be able to provide direct and measurable impact to some of the largest agribusiness players around
We encourage you to submit a cover letter to highlight why you believe that you are a unique fit for our team and what we do! We will read it. Your qualifications and skills
Bachelor's or Master's degree in a quantitative and/or agriculture-related discipline, such as: data science/computer science, crop science/agronomy, animal sciences, geography/GIS, mathematics/statistics, environmental sciences & engineering A ronomy/crop science, animal sciences, field-level yield forecasting experience preferred Geospatial and spatiotemporal analysis, remote sensing, image processing experience preferred Strong analytical and problem-solving skills, paired with the ability to develop creative and efficient solutions Proven experience in applying data science and machine learning techniques to solve real-world problems, preferably in the agriculture sector Strong proficiency in Python to develop clean, efficient pipelines and workflows, ensuring reproducibility and effective team collaboration Experience with version control and frameworks methodologies, preferred Experience with data visualization tools such as Power BI, Tabelau, or similar Strong multitasking and parallel development abilities Strong interpersonal communication skills Creative, naturally curious, and willing to take intellectual risks Able to work under competing, quickly changing priorities, manage expectations effectively and support the team under pressure; highly organized with exceptional attention to detail and follow-through Willingness to travel up to 50% Self-management skills and ability to work as part of an Agile team preferred
Please review the additional requirements regarding essential job functions of McKinsey colleagues.
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